A View of AI that is Neither Left Nor Right

The encyclical about artificial intelligence Magnifica Humanitas, as with all encyclicals since 1891, is going to be misinterpreted using a left-right political framework that reflects the views of readers more than the author.

Catholic social encyclicals since Rerum Novarum (1891) are not predominantly "liberal" nor "conservative" in a modern political sense, though that sometimes is the implication some draw. 

Instead, Catholic social teaching is a consistent body of teaching that deliberately rejects mapping onto left-right categories, emphasizing principles such as:

• human dignity

• the common good

• subsidiarity (decision making as decentralized as possible)

• solidarity

• universal destination of goods. 

The critiques are balanced: unchecked individualism and unrestrained capitalism ("liberalism" in the classical sense) and collectivism or socialism all are said to be problems. 

Catholic social teaching strongly affirms the natural right to private property, for example, as a necessary condition for human freedom, creativity and flourishing. But the principles also include subordinating such rights to use in service of the common good. 

The issue of restraint on compulsion is implicit. Rights to private property are foundational, as a check on a monolithic state usurping all power. But also implicit is the need to voluntarily share resources. 

In other words, private property rights support subsidiarity, the principle that higher authorities should not usurp functions that lower-level bodies (individuals, families, local associations) can perform effectively.

On the other hand, Catholic social teaching also warns against greed, hoarding or political policies that prevent the sharing of outputs. 

Rerum Novarum often is characterized as a seminal document addressing the rise of industrial production. Hence the clear defense of the right to form labor unions. 

But Rerum Novarum also condemned socialism and supported the right of private property ownership, since ownership incentivizes responsibility, creativity, and foresight. 

Without the right to own property, people become dependent wards of the state. Property enables personal initiative and autonomy, which subsidiarity protects. 

Pius XI further formalized subsidiarity in Quadragesimo Anno (1931): higher bodies must not absorb what lower ones can do, as this violates justice and dignity. 

Likewise, Pope John Paul II in Centesimus Annus (1991) reaffirmed that private property maintains "the scope needed for personal and family autonomy" and extends human freedom.

So the balance: Property rights are legitimate, but also a form of stewardship for the common good.

That, in turn, is rooted in a Christian anthropology that views the human person as created in God's image:

• Free

• Responsible

• Creative

• Social.

Interpretations labeling them as "liberal" usually reflect selective readings by contemporary observers, missing the deliberate equilibrium. 

Pope John Paul II in Laborem Exercens, Sollicitudo Rei Socialis and Centesimus Annus: 

• affirmed labor's priority over capital in dignity

• critiqued Western liberal capitalism's excesses

• also criticized Marxist collectivism. 

Centesimus Annus was more open to markets and enterprise but insisted they serve the common good, not as ends in themselves. 

It was interpreted by some as pro-market and by others as critical of capitalism.

Popes Paul VI, Benedict XVI and Francis) also addressed development, peace and protection of the  environment (Laudato Si'), and globalization with similar critiques of materialism, inequality, and ideological extremes.

Magnifica Humanitas updates the tradition for AI. 

It calls for:

• ethical oversight

• regulation focused on human dignity

• the common good

• protecting work/inequality. 

It warns against power concentration (by tech firms or states), biases, job displacement, and treating AI as autonomous, while affirming technology's potential when subordinated to humanity. It invokes subsidiarity (local/intermediary roles, not top-down imposition) and solidarity.

This is not straightforward "AI regulation = liberal" paradigm. 

It supports intervening where markets or tech risk harming dignity, but within a framework upholding private initiative, property (including intellectual), and rejecting total state control or anti-human technocracy.

Selective emphasis is the issue. Media, academics, and activists often highlight critiques of inequality, markets, or corporate power (sounding "left") while downplaying defenses of life, family, religious freedom, subsidiarity, and private property (more "conservative" or traditional).

In polarized “Western” environments, support for unions, welfare elements, or regulation gets labeled "liberal," ignoring the Church's simultaneous opposition to abortion, euthanasia, gender ideology, and excessive statism.

The tradition is transpolitical: how well do policies serve the human person made in God's image? But encyclicals are not policy blueprints.

Catholic social teaching is neither liberal nor conservative. It challenges assumptions about autonomous individualism, materialism and ideological utopias, from the standpoint of protecting human dignity against dehumanizing forces.

It is an affirmation of human creativity, freedom and responsibility. But clearly not an endorsement of any political platforms.

How Zero User Interface Might Work

OpenAI is said to be working on a smartphone optimized for language models, something that might be called a " Zero User Interface" model, where the app-centric mobile environment becomes an agentic experience.

Zero UI represents a fundamental departure from screen-centric interaction, using voice, gesture, sound and biometric signals instead of graphical user interfaces or touchscreens, for example. 

It would represent a fundamental shift in how people interact with technology, much as earlier efforts have focused on form factors including glasses, pins or watches.

Instead of forcing users to navigate complex folder structures and discrete app icons, the device becomes an assistant that understands intent and executes tasks across the digital ecosystem on the user’s behalf, perhaps often without the use of a screen-based interface.

In a smartphone optimized for local language models, the interface moves from "command-based" (where the user clicks icons to trigger features) to "intent-based" (where the user describes the desired outcome).

Traditional UI forced users to know where to click and what to configure, for example. Zero UI systems shift from telling the computer what to do to specifying what outcome is wanted.

Instead of building a spreadsheet to assess customer churn, the user says “show me users who are likely to churn in the next seven days.”

Then the followup prompt might be “recommend the best channel to reach them.”

Without screens, feedback mechanisms become critical. Haptic vibrations in wearables or auditory cues must replace visual confirmations, for example.

• Unified OS-Level Intelligence: Rather than individual apps handling their own data and logic, the LLM acts as a central system service. It can perceive the current state of the device, understand the content on screen, and perform actions—such as sending messages, adjusting settings, or pulling data from services—without the user needing to manually open specific applications.

• Dynamic, Just-in-Time UI: Instead of a static home screen, the device generates interfaces on the fly. If you say, "Show me my budget for this week," it doesn't just open a banking app; it generates a concise, readable summary view tailored to your request, allowing you to act on the information immediately.

• Contextual Awareness: The system learns your routines, habits, and preferences. It becomes predictive—anticipating that you might want your calendar organized after a meeting or that you need specific controls available while you are driving—without needing explicit prompts.

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Without a visual display, the interface relies on glanceability, ambience, and human-centric feedback. 

Input/Output Method	Function
Natural Language (NLP)	Your primary "cursor." You speak, and the model understands nuance, intent, and tone.
Haptic Feedback	Provides non-intrusive alerts. A subtle tap could mean a notification, while a sustained pulse could confirm an action was successfully completed.
Ambient Audio/Chimes	Uses spatial audio and varied tones to provide system status or confirm understanding, reducing the need for constant verbal confirmation.
Gestural Recognition	Using cameras or proximity sensors to interpret hand movements (e.g., a "stop" motion to pause audio, or a "flick" to dismiss a notification).
Ambient LEDs/Light	Subtle light patterns can convey status or urgency, offering a "glanceable" way to understand system states without a full text-based interface.

The greatest hurdle: how do you know what the device can do if there are no menus or icons to guide you?

Successful "Zero UI" devices solve this by:

• Proactive Suggestions: The device doesn't wait to be asked; it learns to surface options when they are contextually relevant (e.g., "Would you like me to book your usual ride home?").

• Conversational Guidance: The AI acts as a guide, periodically informing the user of its capabilities or asking clarifying questions to narrow down intent, effectively "training" the user through natural conversation.

• Standardized Rituals: Just as we learned to "pinch to zoom" on smartphones, screenless devices will likely develop a set of universally understood physical gestures or verbal commands that serve as the "navigation system" of the future.

The idea is to present functions as a fluid, intelligent collaborator, not a collection of app silos, with the objective of minimizing the friction between your intent and the digital outcome.

No Supplier Likes Customer Concentration, But Sometimes It Cannot be Helped

Customer concentration in the hyperscaler segment is practically unavoidable, when a handful of customers represent such a large percentage of the market. 

In 2025, four hyperscalers (Amazon, Google, Microsoft, Meta) represented as much as 70 percent of all capital investment in the technology business, for example. 

Amazon, Google, Meta, and Microsoft control a massive portion of the world's total computing infrastructure, particularly in artificial intelligence. Together with Oracle, these hyperscalers own over 66 percent of global AI compute capacity.

source: Epoch AI 

To be sure, some suppliers (especially those which historically sell to enterprises, mid-market or small business) might not be as dependent on those few customers. 

Still, for some products (graphics processing units, high-performance memory and accelerators, for example), there are just a few big volume buyers. 

And as much as any supplier might wish to reduce reliance on just a few customers, that is hard to do in markets where a handful of buyers dominate:

• Google: Dominates the single-largest share of pure AI compute, holding about 25 percent of global capacity. While rivals rely on Nvidia, Google’s capacity is largely driven by its proprietary Tensor Processing Units (TPUs). In the broader public cloud infrastructure market, Google Cloud holds approximately 14 percent market share

• Microsoft: Holds an estimated 21 percent share of the global cloud infrastructure market. Through massive deployments of Nvidia GPUs and its strategic partnership with OpenAI, Microsoft accounts for an outsized share of enterprise AI workloads in the cloud

• Amazon (AWS): Leads the global cloud computing market with 28 percent market share. While historically focused on broad, general-purpose enterprise computing, Amazon is rapidly scaling its proprietary AI chips (Trainium and Inferentia) to capture a larger portion of specialized AI computing workloads

• Meta: Operating the largest internal compute footprint, Meta owns an estimated 10 percent of the world's total AI compute. Unlike the others, Meta's compute is largely dedicated to internal workloads, such as powering the massive recommendation algorithms for Facebook and Instagram, alongside its proprietary AI models. 

So, like it or not, customer concentration in some parts of the AI value chain is unavoidable. There are only a few “whales” on the buyer side. 

And some might argue there are similar few whales in the anchor tenant category as well. Consider the role OpenAI and Anthropic play as anchor customers for the hyperscaler compute “as a service” providers. 

source: The Information 

I Don't Know How This Changes, But it Has to Do So

In a survey two researchers  conducted in April 2025, they found that 37 percent of Americans reported having had a political breakup, mostly with friends.

That is horrific. It means the tolerance and respect for political differences a democracy might require has been widely abandoned by a significant percentage of citizens. 

 

source: Mertcan Güngör , Peter Ditto 

Of those who reported political breakups, 62 percent had a breakup with a friend (23 percent of the full sample), 40 percent with a family member (15 percent of the sample), 29 percent with a coworker (11 percent of the sample), and 10 percent with a romantic partner (4 percent of the sample), the researchers report. 

Most (56 percent) reported losing more than one kind of relationship (21 percent of the sample). 

In another survey we conducted a week before the 2024 election, 69 percent of breakups happened with a friend; 20 percent happened with a family member, five percent with a romantic partner and two percent with a co-worker. 

Most of us might instinctively recognize that political polarization can weaken a democracy because it turns fellow citizens into enemies. 

In healthy democracies, opposing sides are seen as political adversaries to compete against and at times to negotiate with.In deeply polarized democracies, the other side comes to be seen as an enemy needing to be vanquished.

The danger then is that legitimate policy compromise comes to be seen instead as moral failure. So polarization then leads to, and rewards, extreme positions. And that makes compromise much more difficult, if not impossible. 

Such polarization also erodes trust in government and leaves urgent issues unresolved, as the different factions cannot find common ground. Worse, it leads to violence. 

As for me, I have no choice but to reject polarization. That doesn’t mean accepting a “squishy middle” between the extreme positions of either dominant party. It does mean rejecting the idea that either party any longer has the “best interests of the nation” uppermost and consistently in mind.  

Is Anthropic Worth More than OpenAI?

Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valuing the company at $965 billion post-money, a level that exceeds some estimates of OpenAI valuation of $852 billion.  

The round was co-led by Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN. Significant investors in this round include AMP PBC, Baillie Gifford, Blackstone, Brookfield, D.E. Shaw Ventures, DST Global, Fidelity Management & Research Company, General Catalyst, Insight Partners, Jane Street, Lightspeed Venture Partners, MGX, NTTVC, NX1 Capital, Situational Awareness LP, T. Rowe Price Associates, Inc., T. Rowe Price Investment Management, Inc., and Temasek. It also includes $15 billion of previously committed investments from hyperscalers, including $5 billion from Amazon.

Strategic infrastructure partners Micron, Samsung, and SK hynix also were part of the round. 

Nobody knows whether artificial intelligence is truly a financial bubble, or, if it is, when it bursts. Comparisons to the internet bubble are made and also dismissed, but at least so far, the sometimes-parabolic moves continue.

Much hinges on perceptions of future value. 

And at least so far, nobody wants to fall behind. 

We Might Prefer Income Equality, but Some Amount of Inequality is Needed to Stimulate Supply

Most of us instinctively believe “equality” is a good thing. But there are times when inequality of income might be needed to increase supply of some desired occupations.

And, oftentimes, artificial scarcities are created to support incomes. In fact, many would argue, whatever the stated public benefits (safety, for example), most forms of professional licensing exist, in large part, to protect supplier incomes by creating scarcity.

In large part, the supply of physicians, nurses, and allied health professionals depends on whether the expected lifetime rewards exceed the costs and risks of entering the profession.

But "artificial" scarcity also seems to play a role.

In healthcare, those rewards include not only salary, but also job security, social prestige, autonomy, and meaningful work. Costs include tuition, student debt, years of education, opportunity cost, licensing hurdles, burnout, and malpractice risk.

The central question is straightforward: are the incentives large enough to justify the costs, and are training bottlenecks preventing supply from responding?

Also, are there institutional barriers that artificially create scarcity when abundance is preferred by policymakers and the public, if not desired by a particular industry?

In the United States, the answer is mixed:

• Physician incentives are very strong, but supply is constrained by training bottlenecks that arguably are intentional

• Nursing incentives are moderate, but worsening working conditions and burnout suppress supply

• Technician and allied-health incentives vary widely, often creating shortages in specific specialties.

People choose healthcare careers when lifetime benefits are seen as greater than costs and other burdens.

Benefits might include:

• High pay

• Stable employment

• Prestige and social respect

• Personal fulfillment

• Geographic mobility

Costs include:

• Tuition and debt

• 4 to 12 years of schooling

• Delayed earnings

• Licensing exams and credentialing

• Stress and burnout

When rewards rise, more people enter. When barriers rise, supply tightens. And some of those barriers arguably are intentional. 

Physicians are among the highest-paid professionals in the U.S. Average annual earnings are about $350,000, with specialists earning substantially more, according to the National Bureau of Economic Research, NBER. 

Other strong incentives:

• Exceptional social status

• High job security

• Professional autonomy

• Intellectual challenge

Costs

• Undergraduate degree (4 years)

• Medical school (4 years)

• Residency (3–7 years)

• Often fellowship (1–3 years)

• Student debt commonly exceeds $200,000

• Delayed full earnings until early to mid-30s

The key issue is not lack of interest. There are many qualified applicants. The main constraint is the limited number of residency positions, many of which depend on Medicare-funded graduate medical education, according to NBER. 

That scarcity is intentional, many say. The result is that:

• Very high compensation persists

• Entry remains restricted

• Specialty choice is heavily influenced by income differences

• Shortages occur in primary care and rural practice. 

The picture for nurses is more nuanced.

Registered nurses typically receive:

• Middle- to upper-middle-class incomes

• Strong job security

• Geographic flexibility

• Shorter educational path than physicians. 

Costs include:

• 2–4 years of education

• Licensing requirements

• Moderate student debt.

The primary issue is not educational cost but workplace issues:

• Burnout

• Shift work

• Physical strain

• Violence exposure

• Staffing shortages.

Although nursing remains attractive, retention problems can cause shortages even when many people enter the field.

Related fields include:

• Radiology technologists

• Respiratory therapists

• Laboratory scientists

• Sonographers

• Surgical technologists. 

The incentives are: 

• Shorter training periods

• Stable employment

• Lower debt burdens.

The challenges include:

• Compensation sometimes insufficient relative to specialized skills

• Limited career advancement

• Competition from alternative occupations. 

The results include localized shortages where wages do not adequately compensate for required skills.

The point is that shortages do not necessarily mean compensation is too low.

They often result from:

1. Training bottlenecks

2. Geographic maldistribution

3. Specialty maldistribution

4. Burnout and early retirement

5. Regulatory restrictions. 

Study	Year	Key Finding	Link
National Bureau of Economic Research – Physician Competition: Entry and Substitution	2026	Residency caps and regulatory barriers continue to ration physician supply; mid-level providers expand more rapidly.	NBER paper
National Bureau of Economic Research – Who Values Human Capitalists' Human Capital?	2023	U.S. physicians earn about $350,000 on average; earnings materially affect specialty choice and labor supply.	NBER paper
National Bureau of Economic Research – How Does Provider Supply and Regulation Influence Health Care Market?	2013	Expanded NP and PA autonomy increases effective supply, especially in primary care.	NBER paper
National Bureau of Economic Research – Relaxing Occupational Licensing Requirements	2014	Looser NP regulations reduce prices and expand hours worked by nurse practitioners.	NBER paper
National Bureau of Economic Research – Migration Policy and the Supply of Foreign Physicians	2023	Visa waivers increase physician supply in underserved communities.	NBER paper
Current Programs and Incentives to Overcome Rural Physician Shortages	2023	Loan forgiveness, scholarships, and service obligations improve rural recruitment and retention.	Springer article
Medical Residency Subsidies and Physician Shortages	2025	Expanding residency subsidies increased primary care physician supply by roughly 4% in high-need areas.	Journal of Public Economics article

The point is that to the extent “better healthcare” relies on provider supply, the U.S. healthcare system is understaffed.

Most of the discussion about the state of healthcare seems to focus on insurance. Much less attention seems focused on increasing incentives or decreasing barriers for the supply of healthcare professionals. 

Profession	Financial Incentives	Training Costs	Main Constraint	Supply Outlook
Physicians	Very high	Very high	Residency bottlenecks	Nationally attractive, but constrained
Nurses	Moderate to high	Moderate	Burnout and retention	Cyclical shortages
Allied Health	Moderate	Low to moderate	Uneven pay and capacity	Persistent specialty shortages

Shortages seemingly occur because incentives are not fully aligned with the barriers to producing and retaining healthcare professionals.